We Asked the Author to Explain How He Used AI. A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that. Earlier this month, WIRED published an excerpt from Steve Rosenbaum's buzzy new book,, which looks at how artificial intelligence warps people's sense of reality. Shortly thereafter, The New York Times reported that the book contained over a half-dozen made-up or misattributed quotes.

What counts as an acceptable use of AI has never been fuzzier. Steven Rosenbaum has decided that the real villain behind the bogus quotes in his book is a chatbot. Earlier this week, reported that, Rosenbaum's much-discussed book about how AI shapes reality, contains more than half a dozen fake or misattributed quotes . Rosenbaum pinned some of them on his use of AI. He claimed responsibility for the errors and said he was investigating what went wrong.

Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dataset, causal query, sensitivity level, or treatment require new computation. Here, we instead present an in-context learning approach. Specifically, we propose an amortized approach to causal sensitivity analysis based on prior-data fitted networks. A key challenge is that the sensitivity bounds are not directly available when sampling training data. To address this, we develop a general prior-data construction that is applicable across the class of generalized treatment sensitivity models. Our construction involves a Lagrangian scalarization of the objective to generate training labels for the bounds through a tradeoff between causal effect min/max-imization and sensitivity model violation, which avoids model-specific analytical derivations. We further show that, under standard convexity and linearity conditions, our objective recovers the full Pareto frontier of solutions. Empirically, we demonstrate our amortized approach across various datasets, causal queries, and sensitivity levels, where our approach achieves a test-time computation that is orders of magnitude faster than per-instance methods. To the best of our knowledge, ours is the first foundation model for in-context learning for causal sensitivity analysis.

Conditioning on observations, itdirectly performs inference on the target stochastic process.

Neural processes (NPs) formulate exchangeable stochastic processes and are promising models for meta learning that do not require gradient updates during thetestingphase.

Neural Process (NP) [8] is a class of stochastic processes defined by parametric neural networks.

When Tony Blair looked back on his time in power, he had a simple assessment of his decision to introduce the Freedom of Information Act: "You idiot." While the technology secretary, Peter Kyle, is a fan of the former prime minister, he may be inclined to agree with that verdict after the act was used to reveal that he had been asking ChatGPT which podcasts he should appear on. The disclosure has already caused frustration among ministers, given its possible repercussions. Blair's gripe was that the act risked stopping the frank discussions needed among ministers and officials. Ever since, it has become notoriously difficult to have a freedom of information (FoI) request granted, as officials exploit various legal exemptions to refuse them. The successful use of the legislation to probe into Kyle's AI chatbot use has led some to conclude that a new precedent has been set, one that will have officials across Whitehall sweating over their recent chatbot interactions.

Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservable factors. These sources of uncertainty are often addressed in practice by making strong assumptions about the data-generating mechanism. In this work, we propose a method to compare the predictive performance of decision policies under a variety of modern identification approaches from the causal inference and off-policy evaluation literatures (e.g., instrumental variable, marginal sensitivity model, proximal variable). Key to our method is the insight that there are regions of uncertainty that we can safely ignore in the policy comparison. We develop a practical approach for finite-sample estimation of regret intervals under no assumptions on the parametric form of the status quo policy. We verify our framework theoretically and via synthetic data experiments. We conclude with a real-world application using our framework to support a pre-deployment evaluation of a proposed modification to a healthcare enrollment policy.

Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to draw causal conclusions under unobserved confounding with mathematical guarantees. In this paper, we propose NeuralCSA, a neural framework for generalized causal sensitivity analysis. Unlike previous work, our framework is compatible with (i) a large class of sensitivity models, including the marginal sensitivity model, f-sensitivity models, and Rosenbaum's sensitivity model; (ii) different treatment types (i.e., binary and continuous); and (iii) different causal queries, including (conditional) average treatment effects and simultaneous effects on multiple outcomes. The generality of \frameworkname is achieved by learning a latent distribution shift that corresponds to a treatment intervention using two conditional normalizing flows. We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest and also demonstrate this empirically using both simulated and real-world data.

If you're like me, the growing drip, drip, drip of the content faucet is pushing you to the edge: posts, pings, notifications, alerts. Tech journalist Charles Arthur makes a compelling argument that future content is at a tipping point. Arthur is the author of the substack blog "Social Warming," about social networks' effects on society. "The approaching tsunami of addictive AI-created content will overwhelm us" warns Arthur. The tsunami he points to is the creation of what academics call synthetic media, media that is created entirely by artificial intelligence.